Intercomparison of data-driven and learning-based interpolations of along-track Nadir and wide-swath Swot altimetry observations
Over the last years, a very active field of research aims at exploring new data-driven and learning-based methodologies to propose computationally efficient strategies able to benefit from the large amount of observational remote sensing and numerical simulations for the reconstruction, interpolatio...
Main Authors: | , , , , |
---|---|
Format: | Text |
Language: | English |
Published: |
MDPI
2020
|
Subjects: | |
Online Access: | https://archimer.ifremer.fr/doc/00648/76052/76996.pdf https://archimer.ifremer.fr/doc/00648/76052/ |
id |
fttriple:oai:gotriple.eu:10670/1.7tpedu |
---|---|
record_format |
openpolar |
spelling |
fttriple:oai:gotriple.eu:10670/1.7tpedu 2023-05-15T17:32:07+02:00 Intercomparison of data-driven and learning-based interpolations of along-track Nadir and wide-swath Swot altimetry observations Beauchamp, Maxime Fablet, Ronan Ubelmann, Clément Ballarotta, Maxime Chapron, Bertrand 2020-01-01 https://archimer.ifremer.fr/doc/00648/76052/76996.pdf https://archimer.ifremer.fr/doc/00648/76052/ en eng MDPI 10670/1.7tpedu https://archimer.ifremer.fr/doc/00648/76052/76996.pdf https://archimer.ifremer.fr/doc/00648/76052/ Archimer, archive institutionnelle de l'Ifremer Remote Sensing (2072-4292) (MDPI), 2020-11 , Vol. 12 , N. 22 , P. 3806 (29p.) geo info Text https://vocabularies.coar-repositories.org/resource_types/c_18cf/ 2020 fttriple 2023-01-22T17:02:07Z Over the last years, a very active field of research aims at exploring new data-driven and learning-based methodologies to propose computationally efficient strategies able to benefit from the large amount of observational remote sensing and numerical simulations for the reconstruction, interpolation and prediction of high-resolution derived products of geophysical fields. In this paper, we investigate how they might help to solve for the oversmoothing of the state-of-the-art optimal interpolation (OI) techniques in the reconstruction of sea surface height (SSH) spatio-temporal fields. We focus on two small 10° x 10° GULFSTREAM and 8° x 10° OSMOSIS regions, part of the North-Atlantic basin: the GULFSTREAM area is mainly driven by energetic mesoscale dynamics while OSMOSIS is less energetic but with more noticeable small spatial patterns. Based on Observation System Simulation Experiments (OSSE), we will use the the NATL60 high resolution deterministic ocean simulation of the North Atlantic to generate two types of pseudo altimetric observational dataset: along-track nadir data for the current capabilities of the observation system and wide-swath SWOT data in the context of the upcoming SWOT mission. We briefly introduce the analog data assimilation (AnDA), an up-to-date version of the DINEOF algorithm, and a new neural networks-based end-to-end learning framework for the representation of spatio-temporal irregularly-sampled data. The main objective of this paper consists in providing a thorough intercomparison exercise with appropriate benchmarking metrics to assess if these approaches helps to improve the SSH altimetric interpolation problem and to identify which one performs best in this context. We demonstrate how the newly introduced NN method is a significant improvement with a plug-and-play implementation and its ability to catch up the small scales ranging up to 40km, inaccessible by the conventional methods so far. A clear gain is also demonstrated when assimilating jointly wide-swath SWOT and ... Text North Atlantic Unknown |
institution |
Open Polar |
collection |
Unknown |
op_collection_id |
fttriple |
language |
English |
topic |
geo info |
spellingShingle |
geo info Beauchamp, Maxime Fablet, Ronan Ubelmann, Clément Ballarotta, Maxime Chapron, Bertrand Intercomparison of data-driven and learning-based interpolations of along-track Nadir and wide-swath Swot altimetry observations |
topic_facet |
geo info |
description |
Over the last years, a very active field of research aims at exploring new data-driven and learning-based methodologies to propose computationally efficient strategies able to benefit from the large amount of observational remote sensing and numerical simulations for the reconstruction, interpolation and prediction of high-resolution derived products of geophysical fields. In this paper, we investigate how they might help to solve for the oversmoothing of the state-of-the-art optimal interpolation (OI) techniques in the reconstruction of sea surface height (SSH) spatio-temporal fields. We focus on two small 10° x 10° GULFSTREAM and 8° x 10° OSMOSIS regions, part of the North-Atlantic basin: the GULFSTREAM area is mainly driven by energetic mesoscale dynamics while OSMOSIS is less energetic but with more noticeable small spatial patterns. Based on Observation System Simulation Experiments (OSSE), we will use the the NATL60 high resolution deterministic ocean simulation of the North Atlantic to generate two types of pseudo altimetric observational dataset: along-track nadir data for the current capabilities of the observation system and wide-swath SWOT data in the context of the upcoming SWOT mission. We briefly introduce the analog data assimilation (AnDA), an up-to-date version of the DINEOF algorithm, and a new neural networks-based end-to-end learning framework for the representation of spatio-temporal irregularly-sampled data. The main objective of this paper consists in providing a thorough intercomparison exercise with appropriate benchmarking metrics to assess if these approaches helps to improve the SSH altimetric interpolation problem and to identify which one performs best in this context. We demonstrate how the newly introduced NN method is a significant improvement with a plug-and-play implementation and its ability to catch up the small scales ranging up to 40km, inaccessible by the conventional methods so far. A clear gain is also demonstrated when assimilating jointly wide-swath SWOT and ... |
format |
Text |
author |
Beauchamp, Maxime Fablet, Ronan Ubelmann, Clément Ballarotta, Maxime Chapron, Bertrand |
author_facet |
Beauchamp, Maxime Fablet, Ronan Ubelmann, Clément Ballarotta, Maxime Chapron, Bertrand |
author_sort |
Beauchamp, Maxime |
title |
Intercomparison of data-driven and learning-based interpolations of along-track Nadir and wide-swath Swot altimetry observations |
title_short |
Intercomparison of data-driven and learning-based interpolations of along-track Nadir and wide-swath Swot altimetry observations |
title_full |
Intercomparison of data-driven and learning-based interpolations of along-track Nadir and wide-swath Swot altimetry observations |
title_fullStr |
Intercomparison of data-driven and learning-based interpolations of along-track Nadir and wide-swath Swot altimetry observations |
title_full_unstemmed |
Intercomparison of data-driven and learning-based interpolations of along-track Nadir and wide-swath Swot altimetry observations |
title_sort |
intercomparison of data-driven and learning-based interpolations of along-track nadir and wide-swath swot altimetry observations |
publisher |
MDPI |
publishDate |
2020 |
url |
https://archimer.ifremer.fr/doc/00648/76052/76996.pdf https://archimer.ifremer.fr/doc/00648/76052/ |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
Archimer, archive institutionnelle de l'Ifremer Remote Sensing (2072-4292) (MDPI), 2020-11 , Vol. 12 , N. 22 , P. 3806 (29p.) |
op_relation |
10670/1.7tpedu https://archimer.ifremer.fr/doc/00648/76052/76996.pdf https://archimer.ifremer.fr/doc/00648/76052/ |
_version_ |
1766130073058934784 |